What you would learn in Complete 2-in-1 Python for Business and Finance Bootcamp course?
This is the first-ever complete Python Course for Business and Finance professionals. You will master and learn Python starting from Zero and will be able to master the entire Python Data Science Stack with real-world examples and projects taken out of the Business and Finance world.
It's not just a coding course. It will help you understand and master all the required abstract concepts of the project and code written from beginning to finish.
Important: The quality benchmark for the theory portion can be found in the CFA (Chartered Financial Analyst) Curriculum. The instructor of this course has an MBA in Finance. She has also passed all three CFA Exams. The course we teach will open the door for shady or ill-conceived (but often advertised) practices such as stock price forecasts using LSTM or using prices of stocks within linear regressions.
You'll become an expert, not just in Python Coding but also in
Business and Finance (Time Value of Money Capital Budgeting Risk, Return and Correlation ), Monte Carlo Simulations, Quality and Risk Management in Production and Finance, Mortgage Loans Annuities, Retirement Planning and Portfolio Theory, Portfolio Optimization Factor and Asset Pricing Models Value-at-Risk)
Statistics (descriptive and inferential statistics Hypothesis Testing, Confidence Intervals Normal Distribution and Student's t-distribution, p-value, Bootstrapping Method, Monte Carlo Simulations the Normality of Returns)
Regression (Covariance and Correlation ), Linear Regression, Multiple Regression as well as its dangers and hypothesis testing of Regression Coefficients regression, ANOVA, Dummy Variables Link to Machine Learning and Fama-French Models)
This course is based on an idea that is mutually reinforcing: Learning Python and Theory simultaneously:
Learning Python is more efficient with the proper background and the appropriate examples (avoid toys! ).
The process of learning and mastering the most fundamental concepts and theories of Business, Finance, Statistics, and Regression is much faster and more efficient using Python because you can mode,l visualize, and dynamically explain the underlying concepts behind the theories of math and formulas.
This course will cover in-depth all the relevant and frequently utilized Python Data Science Packages:
Python is derived from beginning to Basics (Standard Library)
Numpy and Scipy for Numeric, Scientific, Financial, and Statistical Coding and Simulations
Pandas can handle, process, clean, aggregate, and alter Tabular (Financial) Data. You deserve more than Excel!
Statsmodels can be used to carry out Regression Analysis as well as hypotheses Testing and ANOVA
Matplotlib, along with Seaborn, is used for the scientific Data Visualization
- Learn Python programming from zero in a Business, Finance & Data Science context (real examples)
- Study about Business & finance (Time Value of Capital Budgeting Return, Risk & Correlation)
- Learn Statistics (descriptive and inferential Probability Distributions and Confidence Intervals Hypothesis Testing)
- Learn how to utilize the Bootstrapping method for hands-on statistical analyses, simulations, and analyses
- Learn Regression (Covariance and Correlation Linear Regression Multiple Regression ANOVA)
- Learn how to make use of the most relevant and effective Python Data Science Packages and Libraries
- Learn to use Numpy and Scipy to perform scientific, financial, and numerical computing.
- Learn to make use of Pandas to handle Tabular (Financial) Information, Cleaning the data, merge it, and manipulating
- Learn to use statistics (scipy) to calculate statistics and Hypothesis Testing
- Learn how to use Statsmodels to perform ANOVA and Regression Analysis and ANOVA
- Learn to make compelling Visualizations and plots using Matplotlib and Seaborn
- Learn how to design user-defined functions for Business & Finance applications
- Learn to design and code real projects that involve Business, Finance & Statistics
- Discover how you can unlock the full potential that is Python and Numpy by using Monte Carlo Simulations
- Know the Sharpe Ratio and write code for it Alpha IRR, Beta, Yield-to-Maturity, Yield-to-Variation (YTM)
- Learn program more sophisticated concepts in Finance Portfolios, Value-at-Risk, as well as (Multiand) Factor Models
- Learn the distinction between the normal distribution and the Student's T-distributions. What to do when
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